An industrial case study of the impact of domain ignorance on the effectiveness of requirements idea generation during requirements elicitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the factors that is supposed to have a significant effect on an individual's effectiveness during requirements engineering activities is knowledge of the problem being solved by the system to be built, i.e., domain knowledge. Nevertheless, domain knowledge is a double-edged sword. While in-depth domain knowledge facilitates understanding the details of the problem, in-depth domain knowledge can promote falling for tacit assumptions of the domain and overlooking the obvious. On the other hand, lack of domain knowledge can facilitate more innovative out-of-the-domain-box idea generation. This paper describes a case study carried out in industry of the idea generation part of a requirements idea brainstorming session conducted by a team deliberately constructed with four domain experts supplied by the company participating in the case study and with four domain ignorants supplied by the authors. The results support the conclusion that having a team consisting of a mix of domain experts and domain ignorants improves the effectiveness of the idea generation part of requirements idea brainstorming.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it